code: def compare_weightedStats(
      model, datafile, xlist, xname, weightV, pdpTDTable, tol=1e-6
  ):
      weightStat = manual_partial_dependence(
          model, datafile, xlist, xname, weightV
      )  # calculate theoretical weighted sts
      wMean = pyunit_utils.extract_col_value_H2OTwoDimTable(
          pdpTDTable, "mean_response"
      )  # stats for age predictor
      wStd = pyunit_utils.extract_col_value_H2OTwoDimTable(
          pdpTDTable, "stddev_response"
      )
      wStdErr = pyunit_utils.extract_col_value_H2OTwoDimTable(
          pdpTDTable, "std_error_mean_response"
      )
      pyunit_utils.equal_two_arrays(
          weightStat[0], wMean, tol, tol, throw_error=True
      )
      pyunit_utils.equal_two_arrays(
          weightStat[1], wStd, tol, tol, throw_error=True
      )
      pyunit_utils.equal_two_arrays(
          weightStat[2], wStdErr, tol, tol, throw_error=True
      )
  
file_path: h2o-3/h2o-py/tests/testdir_misc/pyunit_pubdev_5761_pdp_NA.py
imports: import sys
  import h2o
  import math
  from tests import pyunit_utils
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
  import random
  from h2o.utils.typechecks import is_type
label_desc: Use image node_id 2 for calling a global function with example usage: compare_weightedStats(model, datafile, xlist, xname, weightV, pdpTDTable, tol) without return types